

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase			:				Dict   =     logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[str]								=	field(
							    default='''cifar10'''		,       metadata={'''help''': '''Name of a dataset from the datasets package'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The column name of the images in the files.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''A folder containing the training data.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''A folder containing the validation data.'''}				)
							UpperCAmelCase_						:		Optional[float]								=	field(
							    default=0.15		,       metadata={'''help''': '''Percent to split off of train for validation.'''}				)
							UpperCAmelCase_						:		Optional[int]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		Optional[int]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    }		,       )




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					List[str]      		=					{}
														if self.train_dir is not None:
																					A      :					Union[str, Any]      		=					self.train_dir
														if self.validation_dir is not None:
																					A      :					Any      		=					self.validation_dir
														A      :					int      		=					data_files if data_files else None







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		Optional[str]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Override some existing default config settings when a model is trained from scratch. Example: '''
							            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
							        )
							    }		,       )
							UpperCAmelCase_						:		Optional[str]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}				)
							UpperCAmelCase_						:		str								=	field(
							    default='''main'''		,       metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}		,       )
							UpperCAmelCase_						:		str								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Name or path of preprocessor config.'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
							            '''with private models).'''
							        )
							    }		,       )
							UpperCAmelCase_						:		float								=	field(
							    default=0.75		,       metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}				)







@dataclass
class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		float								=	field(
							    default=1e-3		,       metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}				)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Tuple      		=					torch.stack([example['''pixel_values'''] for example in examples]		)
							return {"pixel_values": pixel_values}






def        snake_case__		(    ):
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							A      :					Optional[Any]      		=					HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)		)
							if len(sys.argv		) == 2 and sys.argv[1].endswith('''.json'''		):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														A		,		A		,		A      :					Tuple      		=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
							else:
														A		,		A		,		A      :					int      		=					parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry('''run_mae'''     ,				lowerCamelCase_     ,				lowerCamelCase_		)

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,				datefmt='''%m/%d/%Y %H:%M:%S'''     ,				handlers=[logging.StreamHandler(sys.stdout		)]     ,				)

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							A      :					Union[str, Any]      		=					training_args.get_process_log_level()
							logger.setLevel(lowerCamelCase_		)
							transformers.utils.logging.set_verbosity(lowerCamelCase_		)
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
							    + F'distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}'		)
							logger.info(F'Training/evaluation parameters {training_args}'		)

							# Detecting last checkpoint.
							A      :					Union[str, Any]      		=					None
							if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
														A      :					Optional[Any]      		=					get_last_checkpoint(training_args.output_dir		)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
																					raise ValueError(
																					    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
																					    '''Use --overwrite_output_dir to overcome.'''		)
														elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																					logger.info(
																					    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
																					    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''		)

    # Initialize our dataset.
							A      :					Dict      		=					load_dataset(
							    data_args.dataset_name     ,				data_args.dataset_config_name     ,				data_files=data_args.data_files     ,				cache_dir=model_args.cache_dir     ,				use_auth_token=True if model_args.use_auth_token else None     ,				)

							# If we don't have a validation split, split off a percentage of train as validation.
							A      :					int      		=					None if '''validation''' in ds.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split     ,				lowerCamelCase_		) and data_args.train_val_split > 0.0:
														A      :					Dict      		=					ds['''train'''].train_test_split(data_args.train_val_split		)
														A      :					Optional[int]      		=					split['''train''']
														A      :					Optional[Any]      		=					split['''test''']

							# Load pretrained model and image processor
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							A      :					Tuple      		=					{
							    '''cache_dir''': model_args.cache_dir,
							    '''revision''': model_args.model_revision,
							    '''use_auth_token''': True if model_args.use_auth_token else None,
							}
							if model_args.config_name:
														A      :					int      		=					ViTMAEConfig.from_pretrained(model_args.config_name     ,				**lowerCamelCase_		)
							elif model_args.model_name_or_path:
														A      :					List[str]      		=					ViTMAEConfig.from_pretrained(model_args.model_name_or_path     ,				**lowerCamelCase_		)
							else:
														A      :					int      		=					ViTMAEConfig()
														logger.warning('''You are instantiating a new config instance from scratch.'''		)
														if model_args.config_overrides is not None:
																					logger.info(F'Overriding config: {model_args.config_overrides}'		)
																					config.update_from_string(model_args.config_overrides		)
																					logger.info(F'New config: {config}'		)

    # adapt config
							config.update(
							    {
							        '''mask_ratio''': model_args.mask_ratio,
							        '''norm_pix_loss''': model_args.norm_pix_loss,
							    }		)

							# create image processor
							if model_args.image_processor_name:
														A      :					Tuple      		=					ViTImageProcessor.from_pretrained(model_args.image_processor_name     ,				**lowerCamelCase_		)
							elif model_args.model_name_or_path:
														A      :					int      		=					ViTImageProcessor.from_pretrained(model_args.model_name_or_path     ,				**lowerCamelCase_		)
							else:
														A      :					Union[str, Any]      		=					ViTImageProcessor()

							# create model
							if model_args.model_name_or_path:
														A      :					Dict      		=					ViTMAEForPreTraining.from_pretrained(
														    model_args.model_name_or_path     ,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)     ,				config=lowerCamelCase_     ,				cache_dir=model_args.cache_dir     ,				revision=model_args.model_revision     ,				use_auth_token=True if model_args.use_auth_token else None     ,				)
							else:
														logger.info('''Training new model from scratch'''		)
														A      :					Tuple      		=					ViTMAEForPreTraining(lowerCamelCase_		)

							if training_args.do_train:
														A      :					Any      		=					ds['''train'''].column_names
							else:
														A      :					str      		=					ds['''validation'''].column_names

							if data_args.image_column_name is not None:
														A      :					int      		=					data_args.image_column_name
							elif "image" in column_names:
														A      :					Union[str, Any]      		=					'''image'''
							elif "img" in column_names:
														A      :					Dict      		=					'''img'''
							else:
														A      :					str      		=					column_names[0]

							# transformations as done in original MAE paper
							# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
							if "shortest_edge" in image_processor.size:
														A      :					List[str]      		=					image_processor.size['''shortest_edge''']
							else:
														A      :					str      		=					(image_processor.size['''height'''], image_processor.size['''width'''])
							A      :					Optional[int]      		=					Compose(
							    [
							        Lambda(lambda lowerCamelCase_		: img.convert('''RGB'''		) if img.mode != "RGB" else img		),
							        RandomResizedCrop(lowerCamelCase_     ,				scale=(0.2, 1.0)     ,				interpolation=InterpolationMode.BICUBIC		),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        Normalize(mean=image_processor.image_mean     ,				std=image_processor.image_std		),
							    ]		)

							def preprocess_images(lowerCamelCase_		):
														A      :					Any      		=					[transforms(lowerCamelCase_		) for image in examples[image_column_name]]
														return examples

							if training_args.do_train:
														if "train" not in ds:
																					raise ValueError('''--do_train requires a train dataset'''		)
														if data_args.max_train_samples is not None:
																					A      :					Dict      		=					ds['''train'''].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
														# Set the training transforms
														ds["train"].set_transform(lowerCamelCase_		)

							if training_args.do_eval:
														if "validation" not in ds:
																					raise ValueError('''--do_eval requires a validation dataset'''		)
														if data_args.max_eval_samples is not None:
																					A      :					str      		=					(
																					    ds['''validation'''].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
																					)
														# Set the validation transforms
														ds["validation"].set_transform(lowerCamelCase_		)

							# Compute absolute learning rate
							A      :					List[Any]      		=					(
							    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
							)
							if training_args.base_learning_rate is not None:
														A      :					List[str]      		=					training_args.base_learning_rate * total_train_batch_size / 256

							# Initialize our trainer
							A      :					Tuple      		=					Trainer(
							    model=lowerCamelCase_     ,				args=lowerCamelCase_     ,				train_dataset=ds['''train'''] if training_args.do_train else None     ,				eval_dataset=ds['''validation'''] if training_args.do_eval else None     ,				tokenizer=lowerCamelCase_     ,				data_collator=lowerCamelCase_     ,				)

							# Training
							if training_args.do_train:
														A      :					Tuple      		=					None
														if training_args.resume_from_checkpoint is not None:
																					A      :					List[Any]      		=					training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					A      :					Optional[int]      		=					last_checkpoint
														A      :					Tuple      		=					trainer.train(resume_from_checkpoint=lowerCamelCase_		)
														trainer.save_model()
														trainer.log_metrics('''train'''     ,				train_result.metrics		)
														trainer.save_metrics('''train'''     ,				train_result.metrics		)
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														A      :					Tuple      		=					trainer.evaluate()
														trainer.log_metrics('''eval'''     ,				lowerCamelCase_		)
														trainer.save_metrics('''eval'''     ,				lowerCamelCase_		)

							# Write model card and (optionally) push to hub
							A      :					Union[str, Any]      		=					{
							    '''tasks''': '''masked-auto-encoding''',
							    '''dataset''': data_args.dataset_name,
							    '''tags''': ['''masked-auto-encoding'''],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**lowerCamelCase_		)
							else:
														trainer.create_model_card(**lowerCamelCase_		)






def        snake_case__		(    lowerCamelCase_		):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()









from math import factorial







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Optional[int]:
														A      :					Union[str, Any]      		=					real
														if isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					str      		=					[1] * rank
														else:
																					A      :					Dict      		=					rank




							def __repr__(					self						)		->       List[str]:
														return (
														    f'{self.real}+'
														    f'{"+".join(str(__UpperCAmelCase						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
														)




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					List[Any]      		=					self.duals.copy()
														while cur[-1] == 0:
																					cur.pop(-1						)
														return Dual(self.real    ,						__UpperCAmelCase						)




							def __add__(					self    ,						__UpperCAmelCase						)		->       Any:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					return Dual(self.real + other    ,						self.duals						)
														A      :					int      		=					self.duals.copy()
														A      :					Any      		=					other.duals.copy()
														if len(__UpperCAmelCase						) > len(__UpperCAmelCase						):
																					o_dual.extend([1] * (len(__UpperCAmelCase						) - len(__UpperCAmelCase						))						)
														elif len(__UpperCAmelCase						) < len(__UpperCAmelCase						):
																					s_dual.extend([1] * (len(__UpperCAmelCase						) - len(__UpperCAmelCase						))						)
														A      :					List[str]      		=					[]
														for i in range(len(__UpperCAmelCase						)						):
																					new_duals.append(s_dual[i] + o_dual[i]						)
														return Dual(self.real + other.real    ,						__UpperCAmelCase						)

							UpperCAmelCase_						:		int								=	__add__




							def __sub__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														return self + other * -1




							def __mul__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					Union[str, Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i * other						)
																					return Dual(self.real * other    ,						__UpperCAmelCase						)
														A      :					Optional[Any]      		=					[0] * (len(self.duals						) + len(other.duals						) + 1)
														for i, item in enumerate(self.duals						):
																					for j, jtem in enumerate(other.duals						):
																												new_duals[i + j + 1] += item * jtem
														for k in range(len(self.duals						)						):
																					new_duals[k] += self.duals[k] * other.real
														for index in range(len(other.duals						)						):
																					new_duals[index] += other.duals[index] * self.real
														return Dual(self.real * other.real    ,						__UpperCAmelCase						)

							UpperCAmelCase_						:		int								=	__mul__




							def __truediv__(					self    ,						__UpperCAmelCase						)		->       Optional[int]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					Optional[Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i / other						)
																					return Dual(self.real / other    ,						__UpperCAmelCase						)
														raise ValueError




							def __floordiv__(					self    ,						__UpperCAmelCase						)		->       Union[str, Any]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					List[Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i // other						)
																					return Dual(self.real // other    ,						__UpperCAmelCase						)
														raise ValueError






							def __pow__(					self    ,						__UpperCAmelCase						)		->       Any:
														if n < 0 or isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					raise ValueError('''power must be a positive integer'''						)
														if n == 0:
																					return 1
														if n == 1:
																					return self
														A      :					Dict      		=					self
														for _ in range(n - 1						):
																					x *= self
														return x






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							if not callable(lowerCamelCase_		):
														raise ValueError('''differentiate() requires a function as input for func'''		)
							if not isinstance(lowerCamelCase_     ,				(float, int)		):
														raise ValueError('''differentiate() requires a float as input for position'''		)
							if not isinstance(lowerCamelCase_     ,				lowerCamelCase_		):
														raise ValueError('''differentiate() requires an int as input for order'''		)
							A      :					Any      		=					Dual(lowerCamelCase_     ,				1		)
							A      :					Dict      		=					func(lowerCamelCase_		)
							if order == 0:
														return result.real
							return result.duals[order - 1] * factorial(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






		def        snake_case__		(    lowerCamelCase_		):
									return y**2 * y**4

		print(differentiate(f, 9, 2))






